The Relation between Types and Frequency of Gambling Activities and Problem Gambling among Women in Canada
Bibliographic record
Abstract
OBJECTIVE: Canada experienced large-scale growth of the gambling industry during the 1990s. Clinical data have indicated that substantial proportions of people seeking help for gambling problems in Canada are women. A population health model was used to understand the relation between types and frequency of gambling activities and problem gambling among women in Canada. METHOD: Data used for the analysis were from the nationally representative Canadian Community Health Survey: Mental Health and Well-Being (CCHS 1.2; n = 10,056, women aged 15 years and older; data collected in 2002). RESULTS: The types of gambling associated with the highest odds of problem gambling among women in Canada were video lottery terminals (VLTs) outside the casino (OR 2.37 to 53.73; P < 0.01), VLTs inside the casino (OR 2.84 to 36.19; P < 0.001), and other casino games (OR 4.01 to 24.15; P < 0.001). CONCLUSIONS: These observations further our understanding of problem gambling among women in Canada and confirm that problem gambling among women is an important public health concern. Frequent VLT gambling, both outside and inside casinos, and other casino games are associated with the largest odds of problem gambling, which highlights an area of gambling in Canada that needs to be reassessed if problem gambling is to be prevented or reduced. Evidence-based research is necessary to inform healthy public policies on gambling in Canada. Findings from the current research have important research and policy implications.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".